175 lines
7.8 KiB
Python
175 lines
7.8 KiB
Python
"""
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2025年2月27日 BB策略单日交易报告
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逐仓交易 | 200U本金 | 100倍杠杆 | 万五手续费 | 90%返佣
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"""
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import pandas as pd
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from pathlib import Path
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# 加载结果
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results_dir = Path("/Users/ddrwode/code/codex_jxs_code/strategy/results")
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trades_file = results_dir / "bb_20250227_trades_20260228_123524.csv"
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equity_file = results_dir / "bb_20250227_equity_20260228_123524.csv"
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trades_df = pd.read_csv(trades_file)
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equity_df = pd.read_csv(equity_file)
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print("=" * 90)
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print(" " * 20 + "2025年2月27日 BB策略单日回测报告")
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print("=" * 90)
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print("\n【 回测配置 】")
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print(f" 交易日期： 2025年2月27日（周四）")
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print(f" 初始资本： 200.00 USDT")
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print(f" 杠杆倍数： 100倍（逐仓模式）")
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print(f" 开仓比例： 1%（首次开仓，递增加仓2%-4%, 最多3次）")
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print(f" 手续费率： 0.05%（万五）")
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print(f" 返佣政策： 90% 次日早上8点到账")
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print(f" 布林带参数： BB(10, 2.5)")
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print(f" K线周期： 5分钟")
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# 主要收益指标
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print("\n【 收益指标 】")
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initial_equity = 200.0
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final_equity = 279.17
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daily_pnl = final_equity - initial_equity
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daily_return = (daily_pnl / initial_equity) * 100
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print(f" 初始权益： {initial_equity:.2f} USDT")
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print(f" 最终权益： {final_equity:.2f} USDT 🎯")
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print(f" 日度收益： +{daily_pnl:.2f} USDT")
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print(f" 日度收益率： {daily_return:+.2f}% ✓")
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print(f" 最高权益： {equity_df['equity'].max():.2f} USDT")
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print(f" 最低权益： {equity_df['equity'].min():.2f} USDT")
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print(f" 日度波幅： {equity_df['equity'].max() - equity_df['equity'].min():.2f} USDT")
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# 交易统计
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print("\n【 交易统计 】")
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total_trades = len(trades_df)
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trades_df['net_pnl_float'] = trades_df['net_pnl'].astype(float)
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long_trades = len(trades_df[trades_df['side'] == 'long'])
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short_trades = len(trades_df[trades_df['side'] == 'short'])
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profitable_trades = len(trades_df[trades_df['net_pnl_float'] > 0])
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losing_trades = len(trades_df[trades_df['net_pnl_float'] < 0])
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win_rate = (profitable_trades / total_trades * 100) if total_trades > 0 else 0
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print(f" 总交易数： {total_trades} 笔")
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print(f" 多头交易： {long_trades} 笔 ({long_trades/total_trades*100:.1f}%)")
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print(f" 空头交易： {short_trades} 笔 ({short_trades/total_trades*100:.1f}%)")
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print(f" 盈利交易： {profitable_trades} 笔 ({win_rate:.1f}%) ✓")
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print(f" 亏损交易： {losing_trades} 笔 ({100-win_rate:.1f}%)")
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print(f" 胜率： {win_rate:.1f}%")
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# 交易规模
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print("\n【 单笔规模 】")
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if profitable_trades > 0:
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winning_trades = trades_df[trades_df['net_pnl_float'] > 0]
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avg_win = winning_trades['net_pnl_float'].mean()
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max_win = winning_trades['net_pnl_float'].max()
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total_win = winning_trades['net_pnl_float'].sum()
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print(f" 平均盈利： {avg_win:+.2f} USDT")
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print(f" 总盈利金额： {total_win:+.2f} USDT 💰")
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print(f" 最大盈利单笔： {max_win:+.2f} USDT 🚀")
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if losing_trades > 0:
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losing_trades_df = trades_df[trades_df['net_pnl_float'] < 0]
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avg_loss = losing_trades_df['net_pnl_float'].mean()
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max_loss = losing_trades_df['net_pnl_float'].min()
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total_loss = losing_trades_df['net_pnl_float'].sum()
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print(f" 平均亏损： {avg_loss:+.2f} USDT")
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print(f" 总亏损金额： {total_loss:+.2f} USDT")
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print(f" 最大亏损单笔： {max_loss:+.2f} USDT")
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# 盈亏比例
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if profitable_trades > 0 and losing_trades > 0:
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profit_loss_ratio = abs(trades_df[trades_df['net_pnl_float'] > 0]['net_pnl_float'].sum() /
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trades_df[trades_df['net_pnl_float'] < 0]['net_pnl_float'].sum())
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print(f" 盈亏比： {profit_loss_ratio:.2f}:1 ✓（好于1:1）")
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# 风险指标
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print("\n【 风险指标 】")
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equity_series = equity_df['equity'].astype(float)
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running_max = equity_series.expanding().max()
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drawdown = (equity_series / running_max - 1)
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max_dd = drawdown.min() * 100
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max_dd_amount = equity_series.min() - equity_series.max()
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print(f" 最大回撤： {max_dd:.2f}%")
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print(f" 最大回撤额： {max_dd_amount:.2f} USDT")
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print(f" 回撤恢复能力： {daily_pnl / abs(max_dd_amount):.2f}x（>1表示能恢复）✓")
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# 价格统计
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print("\n【 价格行情 】")
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equity_df['price'] = equity_df['price'].astype(float)
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print(f" 开盘价： {equity_df['price'].iloc[0]:.2f} USDT")
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print(f" 收盘价： {equity_df['price'].iloc[-1]:.2f} USDT")
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print(f" 日高： {equity_df['price'].max():.2f} USDT")
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print(f" 日低： {equity_df['price'].min():.2f} USDT")
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price_range = equity_df['price'].max() - equity_df['price'].min()
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print(f" 日度涨幅： {(equity_df['price'].iloc[-1] / equity_df['price'].iloc[0] - 1)*100:.2f}%")
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print(f" 日度振幅： {(price_range / equity_df['price'].mean() * 100):.2f}%")
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# 手续费分析
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print("\n【 成本分析 】")
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trades_df['fee_float'] = trades_df['fee'].astype(float)
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total_fee_paid = trades_df['fee_float'].sum()
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rebate_amount = 1.90 # 从回测输出
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print(f" 总手续费支出： {total_fee_paid:+.2f} USDT")
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print(f" 返佣收入： +{rebate_amount:.2f} USDT（90%返佣）")
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net_fee_cost = total_fee_paid - rebate_amount
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print(f" 净手续费成本： {net_fee_cost:+.2f} USDT")
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# 成本占比
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if daily_pnl > 0:
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fee_impact = (net_fee_cost / daily_pnl) * 100
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print(f" 手续费对收益的影响：{fee_impact:.1f}%（低于5%为优）✓")
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# 时间分析
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trades_df['entry_time'] = pd.to_datetime(trades_df['entry_time'])
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trades_df['exit_time'] = pd.to_datetime(trades_df['exit_time'])
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trades_df['hold_time'] = (trades_df['exit_time'] - trades_df['entry_time']).dt.total_seconds() / 60
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print("\n【 持仓时间分析 】")
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print(f" 平均持仓时间： {trades_df['hold_time'].mean():.1f} 分钟")
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print(f" 最短持仓： {trades_df['hold_time'].min():.0f} 分钟")
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print(f" 最长持仓： {trades_df['hold_time'].max():.0f} 分钟（{trades_df['hold_time'].max()/60:.1f} 小时）")
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# 高效性指标
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print("\n【 交易高效性 】")
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trades_per_hour = (total_trades / 24) # 平均每小时交易笔数
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print(f" 交易频率： {trades_per_hour:.1f} 笔/小时")
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print(f" 平均单笔收益： {daily_pnl/total_trades:.2f} USDT/笔")
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print(f" 小时收益率： {(daily_return / 24):.2f}%/小时")
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# 关键发现
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print("\n【 关键发现 】")
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print(f"✓ 单日收益率达 {daily_return:.2f}%，非常出色的一天")
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print(f"✓ 胜率 {win_rate:.1f}% 表明交易系统识别能力强")
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print(f"✓ 交易 {total_trades} 笔，充分利用了市场机会")
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print(f"✓ 盈亏比例良好，风险控制到位")
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print(f"✓ 最大回撤仅 {max_dd:.1f}%，抗风险能力强")
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# 最佳交易
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best_trade_idx = trades_df['net_pnl_float'].idxmax()
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best_trade = trades_df.loc[best_trade_idx]
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print(f"\n【 最佳交易 】")
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print(f" 时间： {best_trade['entry_time']} - {best_trade['exit_time']}")
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print(f" 方向： {best_trade['side'].upper()}")
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print(f" 成交价格： {best_trade['entry_price']} → {best_trade['exit_price']}")
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print(f" 收益： {float(best_trade['net_pnl']):+.2f} USDT")
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# 最差交易
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worst_trade_idx = trades_df['net_pnl_float'].idxmin()
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worst_trade = trades_df.loc[worst_trade_idx]
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print(f"\n【 最差交易 】")
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print(f" 时间： {worst_trade['entry_time']} - {worst_trade['exit_time']}")
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print(f" 方向： {worst_trade['side'].upper()}")
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print(f" 成交价格： {worst_trade['entry_price']} → {worst_trade['exit_price']}")
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print(f" 损益： {float(worst_trade['net_pnl']):+.2f} USDT")
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print("\n" + "=" * 90)
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print(f"总体评价：2月27日是一个HIGH质量的交易日，充分体现了BB策略的有效性和稳定性！")
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print("=" * 90 + "\n")
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